Harmonization of individual case safety reports transmission requirements among PAHO reference authorities: a review of their current regulation
Bibliographic record
Abstract
To perform optimal monitoring of the safety profile in the postmarketing phase, Marketing Authorization Holders and National Regulatory Authorities (NRAs) must evaluate the adverse drug reactions (ADRs) that occurred and characterize their nature, frequency, and severity. Management is possible through Individual Case Safety Reports (ICSRs), which are the reports of organized and processed data. Globally, the International Council for Harmonisation (ICH) E2B guideline suggests harmonized activities for the ICSR electronic content and transmission. In America, the Pan American Health Organization (PAHO) is the agency responsible to implement cooperation among its members, which are recognized as National Regulatory Authorities of Reference (NRARs) such as Argentina, Brazil, Canada, Chile, Colombia, Cuba, Mexico, and the United States. PAHO published the 'Good Pharmacovigilance Practices for the Americas' suggesting improvement and harmonization in the region. After reviewing the regulatory framework, it is assumed that all NRARs have a regulated ICSR transmission system (i.e. a systematic vigilance system for collecting, analyzing, and disseminating information from ADRs). However, significant differences exist, such as the requirement for social media vigilance, expedited and non-expedited ICSRs, coding, severity, and transmission. The volume of ICSRs has significantly increased, due to using electronic standards managed by the NRAs, which facilitates early identification of new ADRs, allowing the implementation of novel minimization activities, contributing to the continuous assessment of the benefit-risk balance of medicines. Nevertheless, there is still area for improvement, especially in Latin America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.109 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".